1. Technical Field
The present disclosure generally relates to methods and processing in the field of seismic data, and particularly to inversion of data sets collected over multiple time periods.
2. Background Information
Traditional approaches to time lapse analysis in oil and gas industry are based on data differencing procedures either in the poststack or prestack domain. Although these methods have provided the oil industry a quantitative approach to evaluate the changes for better reservoir monitoring decisions the analysis suffers from several limitations. These limitations arise primarily due to the practicality of data acquisition and processing steps that makes the data sets incompatible to apply a simple differencing approach.
Time lapse imaging is an established technique in many branches of science and engineering. Practical applications of time lapse can be found in diverse areas such as medical imaging, astronomy, geophysical exploration, hydrology and optical imaging. The fundamental ability to study the dynamic nature of physical processes is a central theme of a time lapse problem. In oil and gas reservoir monitoring, time lapse seismic is well accepted. It is recognized as a key reservoir management tool in the petroleum industry. Several applications such as production changes in reservoir, mapping bypassed oil, monitoring costly injection programs, heavy oil monitoring, time lapse tomography and improving our understanding of reservoir compartmentalization and the fluid flow properties across faults have significantly benefited from time lapse studies commonly known as 4D seismic.
As illustrated in FIG. 1, the basic idea in time-lapse monitoring is to acquire multiple seismic surveys at various times over a known hydrocarbon reservoir 3. The subsurface physical properties caused by fluid flow in the reservoir 3 during oil production may be delineated by the differences in the surveys between one acquisition time to another.
From an acquisition point of view this requires a high degree of repeatability in the seismic acquisition, followed by careful image processing of time lapse data sets to minimize non-repeatable data and processing artifacts in the images. It is followed by calibration of time lapse seismic anomalies with production fluid flow via petrophysical modelling and analysis. Current practice in 4D analysis is based on analysis of data difference directly as seismic amplitude difference maps. Some approaches utilize the amplitude difference maps in two ways: (a) develop a qualitative interpretation indicating possible regions where fluids are flowing in the reservoir, sometimes constrained by supporting data such as geologic control, well logs and engineering production information or (b) invert the amplitude changes from data differences (e.g. subtraction) to obtain maps of pressure and saturation change.
Maps of pressure and saturation change or volumes are calibrated with physical units of saturation and pressure that can assist engineering work flows and reservoir management decisions. Regardless, prior approaches depend on data difference as the input. The latter approach i.e. conversion of the difference amplitude maps to saturation and pressure maps is advocated by many workers in this field and is commonly known as time lapse inversion.
Time-lapse images are often replete with artifacts of whichever time-lapse methodology was undertaken, artifacts that are unrelated to actual flow through the reservoir. These artifacts introduce uncertainty into the interpretation of flow fronts and can greatly diminish the value of the time-lapse data. The artifacts originate from seismic signal and noise which cannot be, but, ideally should be exactly repeated from one survey to the next. Practical limitations of existing seismic acquisition and processing technology limit the repeatability of seismic data.
Current time-lapse imaging methodologies inherently assume perfect seismic repeatability. These methodologies depend on subtraction of data (or images) from repeated surveys to localize changes in properties to the time interval between surveys. These subtraction-based methodologies therefore assume that repeated signal and noise will subtract to zero at positions in the reservoir where fluid saturation and pressure have not changed over time.